Despite being an aging concept, the Dynamic Window Approach [1]} (DWA) is still the most dominant approach to collision-avoiding motion control to date. The DWA uses circular arcs to approximate and predict the otherwise complex trajectories of nonholonomic vehicles. After systematic sampling of such arcs, the best of them is elected according to an objective function that evaluates obstacle clearance and progress towards the target. The elected arc then yields the next velocity command that is sent to the robot. Due to its limited planning horizon, the DWA is susceptible to getting stuck in local minima. To mitigate this issue, the Global Dynamic Window Approach [2]} uses a precomputed Dijkstra heuristic to guide the DWA to the goal. Ögren et al.[3]} harnessed the global DWA scheme and proved convergence in a model predictive control setting. In our previous work, we extended the DWA with predictive collision-avoidance capabilities with regards to moving obstacles [4]}. Our new approach borrows the concept of approximating nonholonomic trajectories with arcs, but uses an informed heuristic search of arbitrary depth to plan ahead instead of the limited horizon of the DWA. This way, the predictive obstacle-avoidance capabilities are significantly improved.
